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README.md

climpyrical


A Python tool for spatially downscaling and reconstructing design value fields using meteorological station observations and CanRCM4 models.

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Setup

git clone https://github.com/pacificclimate/climpyrical/

To install, run

$ pip install climpyrical/

Requirements

To install all of the dependencies used by climpyrical, install from requirements file found in requirements.txt

via

$ pip install -r climpyrical/requirements.txt

climpyrical also requires a version of R be installed with the fields package. To do this, and install R dependencies on a local machine, use

apt install r-base 
Rscript install_pkgs.R r_requirements.txt

To display a progress bar within Jupyter, install the nbwidget extension with

jupyter nbextension enable --py widgetsnbextension
jupyter labextension install @jupyter-widgets/jupyterlab-manager

And restart the jupyter server.

Getting started

Climpyrical was best designed with Jupyter in mind. The interactivity and highly configured nature of the pipeline lends itself well to an interactive interface. Additionally, the pipeline generates a log of each step of the pipeline that can be easily re-run and edited interactively.

The pipeline

1.) preprocess_model.ipynb -> Processes the native CanRCM4 model grid to the target resolution

2.) stations.ipynb -> Processes the stations, does a coordinate transformation

3.) MWOrK.ipynb -> Moving Window Ordinary ratio Kriging (MWOrK) does the

4.) plots.ipynb -> Generate figures from the results

5.) nbcc_stations.ipynb -> Generate individual tables at nbcc locations

6.) combine_tables.ipynb -> Combine each nbcc locations

The first step to running the pipeline is configuring it. Various configurations also need to be added to a configuration yaml file. These contain design value specific information, such as paths to input station and model files, and output filenames. See config_example.yml as a full configuration for running the software on all of the design values.

The recipe is the following, using RL50 as an example:

# Which notebooks to use in the pipeline
steps: [
"preprocess_model.ipynb", 
"stations.ipynb", 
"MWOrK.ipynb", 
"plots.ipynb", 
"nbcc_locations.ipynb", 
"combine_tables.ipynb"
]

# To be placed in climpyrical/
paths:
    output_notebook_path: /intermediate/notebook/logs/path
    preprocessed_model_path: /path/to/folder/
    preprocessed_stations_path: /path/to/folder/
    output_reconstruction_path: /path/to/folder/
    output_tables_path: /path/to/folder/
    output_figure_path: /path/to/folder/
    mask_path: data/masks/canada_mask_rp.nc
    north_mask_path: data/masks/canada_mask_north_rp.nc
    nbcc_loc_path: data/station_inputs/NBCC_2020_new_coords.xlsm

# whether to apply median correction from NBCC 2015
nbcc_mean_correction: True
dvs:
    RL50:
        station_dv: "RL50 (kPa)" # Column header for the design value
        station_path: 'data/station_inputs/sl50_rl50_for_maps.csv' 
        input_model_path: 'data/model_inputs/snw_rain_CanRCM4-LE_ens35_1951-2016_max_rl50_load_ensmean.nc'
        medians: 
            value: 0.4
            action: "multiply"
        fill_glaciers: True

Option 1: Interactive (recommended)

Jupyter Notebooks have been paramaterized using Papermill, so in addition to running them in Jupyter Lab, they can be executed from the terminal. For a tutorial on using Jupyter Lab, you can read their docs.

To reconstruct a design value field, users need a CanRCM4 netCDF design value field as well as an accompanying station data file in the form of a .csv. The user also needs to know the column name of the design value field in the .csv file. These will be configured in the configuration yaml.

The processing notebooks can be found in the following directory:

├── climpyrical
├── notebooks
│   ├── README.ipynb
│   ├── climpyrical_demo.ipynb
...

Open README.ipynb with Jupyter to view detailed instructions on how to reproduce. The notebooks generate a series of files (including intermediate logs) that are laid out in detail in the aforementioned README.ipynb and in pipeline.ipynb.

Option 2: CLI (Command Line Interface) Using Papermill

Since the notebooks are parameterized, they can be run from the command line with Papermill. Papermill produces a log of the notebook once it has been executed. You can select which design values you'd like to run, or which steps you'd like to run from the pipeline in the configuration yaml. Read more about executing notebooks using papermill here.

You supply the configuration yaml using the -f argument.

$[climpyrical/notebooks/] papermill -f config.yml pipeline.ipynb pipeline_log.ipynb

If one wants to run only a segment (or segments) of the pipeline, edit the config.yml file to only include the steps of interest.

i.e.

# Which notebooks to use in the pipeline
steps: [
"preprocess_model.ipynb", 
"stations.ipynb"
]

# To be placed in climpyrical/
paths:
    output_notebook_path: /intermediate/notebook/logs/path
    preprocessed_model_path: /path/to/folder/
    preprocessed_stations_path: /path/to/folder/
    output_reconstruction_path: /path/to/folder/
    output_tables_path: /path/to/folder/
    output_figure_path: /path/to/folder/
    mask_path: data/masks/canada_mask_rp.nc
    north_mask_path: data/masks/canada_mask_north_rp.nc
    nbcc_loc_path: data/station_inputs/NBCC_2020_new_coords.xlsm

# whether to apply median correction from NBCC 2015
nbcc_mean_correction: True
dvs:
    RL50:
        station_dv: "RL50 (kPa)" # Column header for the design value
        station_path: 'data/station_inputs/Interim_snow_rain_load_LR_composite_stations_tbd_v4.csv' 
        input_model_path: 'data/model_inputs/snw_rain_CanRCM4-LE_ens35_1951-2016_max_rl50_load_ensmean.nc'
        medians: 
            value: 0.4
            action: "multiply"
        fill_glaciers: True

Then simply run the command as before:

$[climpyrical/notebooks/] papermill -f config.yml pipeline.ipynb pipeline_log.ipynb

Reading Data --> Put into API documentation

Load an ensemble of climate models using climpyrical's read_data function. read_data creates an xarray dataset containing the fields defined by keys and by the design value key as found in the climate model.

from climpyrical.data import read_data

# necessary keys to load from .nc file

ds = read_data('/path/to/data.nc')

Masking Models

To reamain domain flexible in climpyrical, shapefiles can be provided to mask the analysis to include only modeled values within that shape.

from climpyrical.mask rotate_shapefile, import gen_raster_mask_from_vector

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
can_index = world[world.name == "Canada"].index
can_geom = world.loc[can_index, 'geometry']

rotated_canada = rotate_shapefile(can_geom)

mask = gen_raster_mask_from_vector(ds.rlon, ds.rlat, rotated_canada)

mask contains a 2 dimensional grid with boolean values masked based on the rotated_canada GeoSeries.

More to be found in notebooks/dev/climpyrical_demo.ipynb.

Type Hinting

Climpyrical's functions have opted for type hints rather than a strict checking of each data input and output. It is also meant to help aid in the documentation and understanding of how to use its functions. In general, arrays that are supplied to functions in climpyrical are numpy.ndarrays. These arrays should be of a specific shape (sometimes datatype), in order to play well with the functions. Incorrect array types are usually not strictly enforced within climpyrical itself, and errors about the nature of array inputs are raised mainly in external packages. However, we have included nptyping - a third party type hinting package designed specifically for numpy arrays. We are currently working to have climpyrical's usage (starting with testing) be with mypy which reports errors when the type hinting is not met (aka enforced type hinting).

Examples of understanding numpy typing:

def flatten_coords(
    x: NDArray[(Any, ), float], y: NDArray[(Any, ), float]
) -> Tuple[NDArray[(Any, ), float], NDArray[(Any, ), float]]:
    """Takes the rlat and rlon 1D arrays from the
    NetCDF files for each ensemble member, and creates
    an ordered pairing of each grid cell coordinate in
    rotated pole (rlat, rlon).
    ...

In this simple example above, x and y are numpy NDArray objects of 1 dimension and any length of type float.

An example of 2 dimensional arrays is below:

def interpolate_dataset(
    points: NDArray[(Any, Any), np.float],
    values: NDArray[(Any, Any), np.float],
    target_points: NDArray[(Any, Any), np.float],
    method: str,
) -> NDArray[(Any,), np.float]:

    ...

This function, for example, takes points a 2 dimensional NDArray of any size (in either dimension) and of type float. It returns a 1 dimensional NDArray of any size and type float.

The description of what these variables mean are still found in the docstring of the function itself, however, the intended type of NDArray provided can be found by reading the type hints throughout climpyrical.

Ratio kriging reconstruction

There are a series of notebooks in notebooks that describe how to achieve a ratio reconstruction as demanded by the ratio kriging reconstruction method.

They are meant to be completed in the following order, to acheive results. Please note that this method has only been demonstrated on CanRCM4 gridded models in rotated pole projection, although this software has been adopted to be as flexible as possible to other regions with different projections.

1.) notebooks/mask.ipynb demonstrates constructing a raster mask based on arbitrarily polygons provided. This improves the native CanRCM4 coarse coastlines 2.) notebooks/process_model.ipynb demonstrates the necessary preprocessing steps on the model and writes it to file to be used later on in the method. 3.) notebooks/stations.ipynb demonstrates how to process the station data corresponding to your CanRCM4 model and writes to file 4.) notebooks/ratio_kriging.ipynb performs the ratio kriging method

Additional steps have not been factored out further due to the ever-changing requirements of various design value fields.

Setting up climpyrical for use with rot2reg on Lynx or Leopard

One consistent problem encountered while working on this project, was working to understand the polar stereographic projection that CanRCM4 models are in. This offers several advantages, but can be difficult to understand. A technical overview of this is beyond the scope of this README, however, a guide is included below on how to use climpyrical to perform a transformation from polar stereographic to regular EPSG:4326/WGS84 projection.

This guide is tailored to PCIC internal servers lynx or leopard which has some specific installation features that may or may not be encountered on other machines.

Start in a fresh directory and clone climpyrical into it:

git clone https://github.com/pacificclimate/climpyrical.git 

Create a python3 virtual environment and activate it:

python3 -m venv myvenv
source myvenv/bin/activate

Load the R module into the environment. This step and the next PYTHONPATH steps may be unnecessary for your own machine.

module load R
module load proj/7.2.0

Loading the R module also loads gdal/2.2.3 which sets the PYTHONPATH env variable interfering with our virtual python environment. So it's important to unset PYTHONPATH before proceeding:

unset PYTHONPATH

Verify you are still in your virtual environment:

which python
> ...myvenv/bin/python

Next install climpyrical's requirements with:

pip install cython
pip install -r climpyrical/requirements.txt

Then finally install climpyrical with:

pip install -e climpyrical/

Now you can use climpyrical. To unrotate a CanRCM4 file and write it to a new netCDF4 file, simply:

python climpyrical/cmd/rot2reg.py "path/to/input_CanRCM4.nc" "path/to/output_CanRCM4.nc"

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A Python tool for constraining and manipulating CanRCM4 climate models.

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